A model of self-organizing spatio-temporal receptive fields is proposed. It consists of a one-layer feed forward network with multiple delay channels. Every weight of the network is modified according to Hebb-type learning algorithm proposed by Sanger. The network is trained with random Gaussian noise inputs with nonzero mean. It is shown that a variety of spatio-temporal receptive fields are acquired by this network. Some of them have similar properties to visual neurons found in mammalian retina, especially to X- and Y-type ganglion cells.